When are you taking us outside? An exploratory study of the integration of the outdoors in preschool and primary education in Quebec
Bibliographic record
Abstract
en développement curriculaire (CUDC), Université du Québec à Montréal, Montréal, QC, Canada3 Centre de Recherche Interdisciplinaire sur la Formation et la Profession Enseignante (CRIFPE), Université du Québec à Montréal, Montréal, QC, Canada4 École de Santé Publique de l’Université de Montréal (ESPUM), Montréal, QC, Canada 5 Centre de recherche de l’Institut de Santé Mentale de Montréal (CRIUSMM), Montréal, QC, CanadaABSTRACTIntroduction: Recent research investigating the benefits of being outdoors and contact with nature in children showed strong associations with children’s health and development. More teachers are choosing to integrate outdoor learning (OL) into their practice in Quebec, but few studies have focused on OL in the school environment, particularly in Canada and more specifically in Quebec, despite the fact that the school context lends itself favorably to this practice. Objective: The purpose of this study was to portray OL in preschool and primary schools in Québec by identifying three key elements: 1) teachers’ perception of OL, 2) its uses in schools, and 3) teaching strategies and factors that influence teachers’ integration of OL. Methodology: Semi-structured group interviews (n=4) conducted with 14 teachers and participant observations (n=4) were used for data collection. Inclusion criteria were to be a preschool or primary school teacher, to have taught at least eight sessions of OL in the past year, and to have no connection or contact with the research team prior to the start of the study. Results: First, the results showed that teachers commonly understood outdoor as being in the open air, practicing a physical activity, the presence of nature, having educational freedom and an intended objective. Second, teachers appeared to incorporate a variety of pedagogical intentions in OL (e.g., environmental awareness, interdisciplinary learning), in a variety of settings (e.g., city parks, woodlands), and with a variety of academic subjects and learning tasks (e.g., French, mathematics). Third, teachers used a wide range of teaching strategies in OL (e.g., having flexible planning and well-established routines). Participants also identified multiple factors specific to their setting that appeared to facilitate (e.g., parental support) or limit (e.g., storage of materials) their integration of OL into the school environment. Conclusion: This study provided a better understanding of the current use of the OL in the Quebec school environment by identifying the common characteristics, limitations and winning strategies of its use in schools. Teachers and schools interested in OL could benefit from the results of this study, particularly those interested in adopting a Forest School or Udeskole approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".